Creating Activity Indicators

Activity Indicators are integral features that provide real-time performance insights on top of your customer data. These values are stored as aggregation attributes for each Customer Profile, making them accessible for any form of activation, be that Audiences, AI Workbench, or CX Flows and Journeys.

An Activity Indicator is a calculated attribute or metric that provides insights about the duration, frequency, or volume of customer interactions with a brand. They measure the performance of business areas based on customer activities and are recalculated when relevant application events are ingested for a given customer profile.

Activity Indicators serve as building blocks for CX Flows, CX Journey milestones, audience building, customer segmentation, and even other Calculated Indicators. For instance, a "number of urgent service calls" activity indicator aggregates the number of times a customer calls for service, facilitating customer segmentation into higher service levels or triggering a journey milestone.

Each Activity Indicator in the CDP consists of three parts:

Logical or Calculated Evaluation

This involves applying calculation methods to activity attributes. Methods include Count, Occurrences, Aggregation (Sum, Minimum/Maximum), Standard Deviation, and Average.

Date Range

A time-based criterion applied to the activity indicator calculation.

Condition Criteria

These are 'And/Or' conditions and operators applied before calculation to ensure only relevant activities count towards the indicator. For example, applying the condition "order value is above 100."

When an event, like a purchase order, is ingested for a customer:

• Conditions are applied, filtering the activity (e.g., only orders with total value above 100).
• The calculation method is applied (e.g., counting the number of orders).

Every activity automatically creates an indicator, which acts as a counter for the total number of times the activity occurs for the customer. This is named "Total", followed by the activity name, such as Total Orders. Activity Indicators can be used in segment calculations and CX Flows, allowing for real-time, actionable insights to drive a company’s strategic and operational decisions.

An evaluation, or re-calculation of activity indicators takes place at the following times:

• When you create or edit an activity indicator. The entire customer userbase is evaluated to check whether existing customers match the current indicator.
• Each time an application event is ingested into the CDP, the related or matched customers are evaluated to check which activities they belong to.
• If the "Within the last" time range or condition is applied, the activity indicator is recalculated once a day based on the time frame.

Please note that the re-calculation of activity indicators may trigger existing customer journeys, as those customers are now either included or excluded from the activity indicator after the re-calculation.

SAP Customer Data Platform (CDP) extends beyond basic activity indicators by incorporating Calculated and Predictive Indicators, offering deeper data insights for strategic decision-making.

Calculated Indicators

Calculated Indicators perform complex computations by integrating multiple data elements such as activity indicators, attributes, and conditions into a single metric. They come in five subtypes, including Custom Indicator, RFM (Recency, Frequency, Monetary) Indicator, RFE (Recency, Frequency, Engagement) Indicator, Child Indicator and Recurring Date Indicator, enabling tailored analytics to fit specific business needs.

Predictive Indicators

Predictive Indicators use machine learning to predict customer behaviors and trends. SAP CDP supports models like Churn, Propensity to Buy, Customer Lifetime Value, and True Customer Lifetime Value, providing foresight that informs proactive business strategies.

In this lesson, you learned about obtaining real-time performance insights from your customer data using Activity Indicators. You now understand how some of these indicators offer calculated metrics that reveal the duration, frequency, or volume of a customer’s interaction with your brand, while others use machine learning to predict customer behaviors and trends. Both these metrics can help you build audiences, segment customers, and create CX Flows and Journey milestones.